Articles | Volume 19, issue 11
https://doi.org/10.5194/amt-19-3875-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/amt-19-3875-2026
© Author(s) 2026. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
A new approach to inversion of multi-spectral data with applications to FUV remote sensing
Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, 80309, USA
Tomoko Matsuo
Ann & H.J. Smead Department of Aerospace Engineering Sciences, University of Colorado Boulder, Boulder, CO, 80309, USA
Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, 80309, USA
William Kleiber
Department of Applied Mathematics, University of Colorado Boulder, Boulder, CO, 80309, USA
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Edward H. Bair, Jeff Dozier, Karl Rittger, Timbo Stillinger, William Kleiber, and Robert E. Davis
The Cryosphere, 17, 2629–2643, https://doi.org/10.5194/tc-17-2629-2023, https://doi.org/10.5194/tc-17-2629-2023, 2023
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To test the title question, three snow cover products were used in a snow model. Contrary to previous work, higher-spatial-resolution snow cover products only improved the model accuracy marginally. Conclusions are as follows: (1) snow cover and albedo from moderate-resolution sensors continue to provide accurate forcings and (2) finer spatial and temporal resolutions are the future for Earth observations, but existing moderate-resolution sensors still offer value.
Álvaro Ossandón, Manuela I. Brunner, Balaji Rajagopalan, and William Kleiber
Hydrol. Earth Syst. Sci., 26, 149–166, https://doi.org/10.5194/hess-26-149-2022, https://doi.org/10.5194/hess-26-149-2022, 2022
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Timely projections of seasonal streamflow extremes on a river network can be useful for flood risk mitigation, but this is challenging, particularly under space–time nonstationarity. We develop a space–time Bayesian hierarchical model (BHM) using temporal climate covariates and copulas to project seasonal streamflow extremes and the attendant uncertainties. We demonstrate this on the Upper Colorado River basin to project spring flow extremes using the preceding winter’s climate teleconnections.
Clayton Cantrall and Tomoko Matsuo
Atmos. Meas. Tech., 14, 6917–6928, https://doi.org/10.5194/amt-14-6917-2021, https://doi.org/10.5194/amt-14-6917-2021, 2021
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This paper presents a new technique to determine temperature in the thermosphere from observations of far ultraviolet radiation emitted by molecular nitrogen. The technique utilizes a ratio of two far ultraviolet spectral channels to capture the thermosphere temperature signal. Applying the technique to NASA GOLD observations results in temperatures that agree well with other thermosphere observations during a geomagnetic disturbance.
Zofia Stanley, Ian Grooms, and William Kleiber
Nonlin. Processes Geophys., 28, 565–583, https://doi.org/10.5194/npg-28-565-2021, https://doi.org/10.5194/npg-28-565-2021, 2021
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In weather forecasting, observations are incorporated into a model of the atmosphere through a process called data assimilation. Sometimes observations in one location may impact the weather forecast in another faraway location in undesirable ways. The impact of distant observations on the forecast is mitigated through a process called localization. We propose a new method for localization when a model has multiple length scales, as in a model spanning both the ocean and the atmosphere.
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Short summary
We propose a new approach for inverse problems involving ratios of photon counts. We show that the method is computationally efficient and accurately handles the uncertainty introduced by count data. We demonstrate the method by estimating the temperature in the upper atmosphere in both calm and geomagnetically active conditions. We also present results that suggest this method can allow extension of these techniques to low signal to noise scenarios.
We propose a new approach for inverse problems involving ratios of photon counts. We show that...